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Named entity recognition, and other information extraction tasks, frequentlyuse linguistic features such as part of speech tags or chunkings. For languageswhere word boundaries are not readily identified in text, word segmentation isa key first step to generating features for an NER system. While using wordboundary tags as features are helpful, the signals that aid in identifyingthese boundaries may provide richer information for an NER system. Newstate-of-the-art word segmentation systems use neural models to learnrepresentations for predicting word boundaries. We show that these samerepresentations, jointly trained with an NER system, yield significantimprovements in NER for Chinese social media. In our experiments, jointlytraining NER and word segmentation with an LSTM-CRF model yields nearly 5%absolute improvement over previously published results.

Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning